In 2023, the violent crime rate in the United States was 363.8 cases per 100,000 of the population. Even though the violent crime rate has been decreasing since 1990, the United States tops the ranking of countries with the most prisoners. In addition, due to the FBI's transition to a new crime reporting system in which law enforcement agencies voluntarily submit crime reports, data may not accurately reflect the total number of crimes committed in recent years. Reported violent crime rate in the United States The United States Federal Bureau of Investigation tracks the rate of reported violent crimes per 100,000 U.S. inhabitants. In the timeline above, rates are shown starting in 1990. The rate of reported violent crime has fallen since a high of 758.20 reported crimes in 1991 to a low of 363.6 reported violent crimes in 2014. In 2023, there were around 1.22 million violent crimes reported to the FBI in the United States. This number can be compared to the total number of property crimes, roughly 6.41 million that year. Of violent crimes in 2023, aggravated assaults were the most common offenses in the United States, while homicide offenses were the least common. Law enforcement officers and crime clearance Though the violent crime rate was down in 2013, the number of law enforcement officers also fell. Between 2005 and 2009, the number of law enforcement officers in the United States rose from around 673,100 to 708,800. However, since 2009, the number of officers fell to a low of 626,900 officers in 2013. The number of law enforcement officers has since grown, reaching 720,652 in 2023. In 2023, the crime clearance rate in the U.S. was highest for murder and non-negligent manslaughter charges, with around 57.8 percent of murders being solved by investigators and a suspect being charged with the crime. Additionally, roughly 46.1 percent of aggravated assaults were cleared in that year. A statistics report on violent crime in the U.S. can be found here.

This dataset contains different collected datasets with crime data of many large cities. Below are the descriptions for each seperate dataset. Note: Dataset properties and column may differ from each other since the information was collected by the local police in different styles and situations.

1. Los Angeles

The Los Angeles dataset has the collected data on different crimes that happened in Los Angeles from 2000 up until May 2024. The columns are as follows:

• DR_NO - Division of Records Number: Official file number made up of a 2 digit year, area ID, and 5 digits

• Date Rptd - The date when the police found out about the crime

• Date OCC - The actual date of the crime

• Time OCC - In military time

• Area - The LAPD has 21 Community Police Stations referred to as Geographic Areas within the department. These Geographic Areas are sequentially numbered from 1-21.

• Area Name - The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for. For example 77th Street Division is located at the intersection of South Broadway and 77th Street, serving neighborhoods in South Los Angeles.

• Rpt Dist No - A four-digit code that represents a sub-area within a Geographic Area. All crime records reference the "RD" that it occurred in for statistical comparisons. Find LAPD Reporting Districts on the LA City GeoHub at http://geohub.lacity.org/datasets/c4f83909b81d4786aa8ba8a74a4b4db1_4

• Crm Cd - Indicates the crime committed. (Same as Crime Code 1)

• Crm Cd Desc - Defines the Crime Code provided.

• Mocodes - Modus Operandi: Activities associated with the suspect in commission of the crime.

• Vict Age - The age of the victim

• Vict Sex - The gender of the victim. They are as follows:

  • M - Male
  • F - Female
  • X - Unknown

• Vict Descent - Descent Code:

  • A - Other Asian
  • B - Black
  • C - Chinese
  • D - Cambodian
  • F - Filipino
  • G - Guamanian
  • H - Hispanic/Latin/Mexican
  • I - American Indian/Alaskan Native
  • J - Japanese
  • K - Korean
  • L - Laotian
  • O - Other
  • P - Pacific Islander
  • S - Samoan
  • U - Hawaiian
  • V - Vietnamese
  • W - White
  • X - Unknown
  • Z - Asian Indian

• Premis Cd - The type of structure, vehicle, or location where the crime took place.

• Premis Desc - Defines the Premise Code provided.

• Weapon Used Cd - The type of weapon used in the crime.

• Status - Status of the case. (IC is the default)

• Status Desc - Defines the Status Code provided.

• Crm Cd 1 - Indicates the crime committed. Crime Code 1 is the primary and most serious one. Crime Code 2, 3, and 4 are respectively less serious offenses. Lower crime class numbers are more serious.

• Crm Cd 2 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 3 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 4 - May contain a code for an additional crime, less serious than Crime Code 1.

• Location - Street address of crime incident rounded to the nearest hundred block to maintain anonymity.

• Cross Street - Cross Street of rounded Address

• LAT - Latitude

• LON - Longitude

This dataset has 28 columns and 944K rows. I hope you will find it useful. God bless you

1. Chicago

This dataset contains crime data on Chicago, from 2001 to present. The columns are as follows:

• ID - Unique Identifier for the record

• Case Number - The Chicago Police Department RD Number (Records Division Number), which is unique to the incident.

• Date - Date when the incident occurred. this is sometimes a best estimate.

• Block - The partially redacted address where the incident occurred, placing it on the same block as the actual address.

• IUCR - The Illinois Unifrom Crime Reporting code. This is directly linked to the Primary Type and Description. See the list of IUCR codes at https://data.cityofchicago.org/d/c7ck-438e..

• Primary Type - The primary description of the IUCR code.

• Description - The secondary description of the IUCR code, a subcategory of the primary description.

• Location Description - Description of the location where the incident occurred.

• Arrest - Indicates whether an arrest was made.

• Domestic - Indicates whether the incident was domestic-related as defined by the Illinois Domestic Violence Act.

• Beat - Indicates the beat where the incident occurred. A beat is the smallest police geographic area – each beat has a dedicated police beat car. Three to five beats make up a police sector, and three sectors make up a police district. The Chicago Police Department has 22 police districts. See the beats at https://data.cityofchicago.org/d/aerh-rz74.

• Distric...

This dataset includes all criminal offenses reported to the Colorado Springs Police Department. Each case report (incident) may have several offenses. Each offense may have multiple suspects and/or victims.

Important: This dataset provided by CSPD does not apply the same counting rules as official data reported to the Colorado Bureau of Investigations and the Federal Bureau of Investigation. This means comparisons to those datasets would be inaccurate.

Prelude

This dataset is a cleaned version of the Chicago Crime Dataset, which can be found here. All rights for the dataset go to the original owners. The purpose of this dataset is to display my skills in visualizations and creating dashboards. To be specific, I will attempt to create a dashboard that will allow users to see metrics for a specific crime within a given year using filters and metrics. Due to this, there will not be much of a focus on the analysis of the data, but there will be portions discussing the validity of the dataset, the steps I took to clean the data, and how I organized it. The cleaned datasets can be found below, the Query (which utilized BigQuery) can be found here and the Tableau dashboard can be found here.

About the Dataset

Important Facts

The dataset comes directly from the City of Chicago's website under the page "City Data Catalog." The data is gathered directly from the Chicago Police's CLEAR (Citizen Law Enforcement Analysis and Reporting) and is updated daily to present the information accurately. This means that a crime on a specific date may be changed to better display the case. The dataset represents crimes starting all the way from 2001 to seven days prior to today's date.

Reliability

Using the ROCCC method, we can see that: * The data has high reliability: The data covers the entirety of Chicago from a little over 2 decades. It covers all the wards within Chicago and even gives the street names. While we may not have an idea for how big the sample size is, I do believe that the dataset has high reliability since it geographically covers the entirety of Chicago. * The data has high originality: The dataset was gained directly from the Chicago Police Dept. using their database, so we can say this dataset is original. * The data is somewhat comprehensive: While we do have important information such as the types of crimes committed and their geographic location, I do not think this gives us proper insights as to why these crimes take place. We can pinpoint the location of the crime, but we are limited by the information we have. How hot was the day of the crime? Did the crime take place in a neighborhood with low-income? I believe that these key factors prevent us from getting proper insights as to why these crimes take place, so I would say that this dataset is subpar with how comprehensive it is. * The data is current: The dataset is updated frequently to display crimes that took place seven days prior to today's date and may even update past crimes as more information comes to light. Due to the frequent updates, I do believe the data is current. * The data is cited: As mentioned prior, the data is collected directly from the polices CLEAR system, so we can say that the data is cited.

Processing the Data

Cleaning the Dataset

The purpose of this step is to clean the dataset such that there are no outliers in the dashboard. To do this, we are going to do the following: * Check for any null values and determine whether we should remove them. * Update any values where there may be typos. * Check for outliers and determine if we should remove them.

The following steps will be explained in the code segments below. (I used BigQuery for this so the coding will follow BigQuery's syntax) ```

Examining the dataset

There are over 7.5 million rows of data

Putting a limit so it does not take a long time to run

SELECT * FROM portfolioproject-350601.ChicagoCrime.Crime LIMIT 1000;

Seeing which points are null

There are 85,000 null points so we can exclude them as it's not a significant amount since it is only ~1.3% of the dataset

Most of the null points are in the lat and long, which we will need later

Because we don't have the full address, we can't estimate the lat and long in SQL so we will have to delete the rows with Null Data

SELECT * FROM portfolioproject-350601.ChicagoCrime.Crime WHERE unique_key IS NULL OR case_number IS NULL OR date IS NULL OR primary_type IS NULL OR location_description IS NULL OR arrest IS NULL OR longitude IS NULL OR latitude IS NULL;

Deleting all null rows

DELETE FROM portfolioproject-350601.ChicagoCrime.Crime WHERE
unique_key IS NULL OR case_number IS NULL OR date IS NULL OR primary_type IS NULL OR location_description IS NULL OR arrest IS NULL OR longitude IS NULL OR latitude IS NULL;

Checking for any duplicates in the unique keys

None to be found

SELECT unique_key, COUNT(unique_key) FROM `portfolioproject-350601.ChicagoCrime....

The RMS Crime Incidents dataset consists of crime reports from the Detroit Police Department Records Management System (RMS). This data reflects criminal offenses reported in the City of Detroit that DPD was involved in from December 2016 to present. Note that records are included in the dataset based on when an incident is reported which could result in an occurrence date before December 2016. Incident data is typically entered into mobile devices by the officer in the field when responding to an incident. Incidents that occurred in Detroit but in a location that is under the jurisdiction of the Michigan State Police (MSP) or Wayne State University Police Department (WSUPD), such as on an expressway, Belle Isle, or around Wayne State University, are included only if the incident is handled by DPD. Such records are reviewed in a monthly audit to ensure that the incidents are counted by one and only one agency (MSP or DPD). This data is updated daily. For each crime incident, one or more offense charges are recorded, and each row in the dataset corresponds with one of these charges. An example could be a domestic assault where property was also vandalized. Offense charges that occurred at the same crime incident share a common incident number. For each offense charge record (rows)details include when and where the incident occurred, the nature of the offense, DPD precinct or detail, and the case investigation status. Locations of incidents associated with each call are reported based on the nearest intersection to protect the privacy of individuals.RMS Crime Incident data complies with Michigan Incident Crime Reporting (MICR) standards. More information about MICR standards is available via the MICR Website. The Manual and Arrest Charge Code Card may be especially helpful. There may be small differences between RMS Crime Incident data shared here and data shared through MICR given data presented here is updated here more frequently which results in a difference in a cadence of status updates. Additionally, this dataset includes crime incidents that following an investigation are coded with a case status of ‘Unfounded’. In most cases, this means that the incident occurred outside the jurisdiction of DPD or otherwise was reported in error. The State of Michigan, through the MICR program, reports data to the National Incident-Based Reporting System (NIBRS).Yearly Datasets for RMS Crime Incidents have been added to the ODP. This is to improve the user's experience in handling the large file size of the records in the comprehensive dataset. You may download each year separately, which significantly reduces the size and records for each file. In addition to the past years, we have also included a year-to-date dataset. This captures all RMS Crime Incidents from January 1, 2025, to present.Should you have questions about this dataset, you may contact the Commanding Officer of the Detroit Police Department's Crime Data Analytics at 313-596-2250 or CrimeIntelligenceBureau@detroitmi.gov.

📘 About the Dataset

This dataset contains detailed records of crimes reported to the Los Angeles Police Department (LAPD) from 2020 to the present. It includes information about the type of crime, when and where it occurred, the victim’s demographic details, the weapon used (if any), and the status of the investigation. The dataset is useful for: Crime trend analysis Victim-focused studies Location-based risk assessment Vehicle-related crime insights Understanding contributing factors such as weapons, MO codes, and case status It also helps build projects related to data analytics, machine learning, pattern detection, risk forecasting, and urban safety studies.

📂 What the Dataset Includes Key columns include: DATE OCC & DATE RPTD – When the crime happened and when it was reported Crm Cd / Crime Category – Type of crime AREA / Area Name – LAPD division where the incident occurred Victim Age, Gender, and Descent Weapon Used (if applicable) MO Codes – Method of Operation, describing how the crime was carried out Premise Code – Location type (street, residence, parking lot, etc.) Status – Case outcome (Investigation Continued, Adult Arrest, etc.) Vehicle-related fields (for theft and break-ins)

⭐ Why This Dataset Is Valuable Covers millions of crime records over multiple years Updated regularly by the LAPD Suitable for EDA, visualization, predictive modeling, and geospatial analysis Granular information helps identify patterns across time, location, victims, and crime methods

🔗 Original Source

This dataset is sourced from the U.S. Government open data portal: https://catalog.data.gov/dataset/crime-data-from-2020-to-present

This dataset includes all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department (NYPD) for all complete quarters so far this year (2017). For additional details, please see the attached data dictionary in the ‘About’ section.

THIS DATASET WAS LAST UPDATED AT 7:11 AM EASTERN ON DEC. 1

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that have occurred in the City of Chicago over the past year, minus the most recent seven days of data. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited.

The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://bit.ly/rk5Tpc.

Number, percentage and rate (per 100,000 population) of homicide victims, by racialized identity group (total, by racialized identity group; racialized identity group; South Asian; Chinese; Black; Filipino; Arab; Latin American; Southeast Asian; West Asian; Korean; Japanese; other racialized identity group; multiple racialized identity; racialized identity, but racialized identity group is unknown; rest of the population; unknown racialized identity group), gender (all genders; male; female; gender unknown) and region (Canada; Atlantic region; Quebec; Ontario; Prairies region; British Columbia; territories), 2019 to 2024.

Context

With this dataset I hope to raise awareness on the trends in crime.

Content

For NYPD Complaint Data, each row represents a crime. For information on the columns, please see the attached csv, "Crime_Column_Description". Reported crime go back 5 years but I only attached reported crime from 2014-2015 due to file size. The full report can be found at NYC Open Data (https://data.cityofnewyork.us/Public-Safety/NYPD-Complaint-Data-Historic/qgea-i56i)

Acknowledgements

I would like to thank NYC Open Data for the dataset.

Inspiration

Additional things I would like to better understand: 1. Differences in crime that exist between the 5 boroughs 2. A mapping of the crimes per borough 3. Where do the most dangerous crimes happen and what time?

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study was a secondary analysis of data from the National Crime Victimization Survey (NCVS) and National Incidents Based Reporting System (NIBRS) for the period 1998-2007. The analysis calculates two separate measures of the incidents of violence that occurred during burglaries. The study addressed the following research questions: Is burglary a violent crime? Are different levels of violence associated with residential versus nonresidential burglaries? How frequently is a household member present during a residential burglary? How frequently does violence occur in the commission of a burglary? What forms does burglary-related violence take? Are there differences in rates of violence between attempted and completed burglaries? What constitutes the crime of burglary in current statutory law? How do the federal government and the various states define burglary (grades and elements)? Does statutory law comport with empirical observations of what the typical characteristics of acts of burglary are? The SPSS code distributed here alters an existing dataset drawn from pre-existing studies. In order to use this code users must first create the original data file drawn from National Crime Victimization Survey (NCVS) and National Incidents Based Reporting System (NIBRS) data from the period of 1998-2007. All data used for this study are publicly available through ICPSR. See the variable description section for a comprehensive list of, and direct links to, all datasets used to create this original dataset.

In response to a growing concern about hate crimes, the United States Congress enacted the Hate Crime Statistics Act of 1990. The Act requires the attorney general to establish guidelines and collect, as part of the Uniform Crime Reporting (UCR) Program, data "about crimes that manifest evidence of prejudice based on race, religion, sexual orientation, or ethnicity, including where appropriate the crimes of murder and non-negligent manslaughter, forcible rape, aggravated assault, simple assault, intimidation, arson, and destruction, damage or vandalism of property." Hate crime data collection was required by the Act to begin in calendar year 1990 and to continue for four successive years. In September 1994, the Violent Crime Control and Law Enforcement Act amended the Hate Crime Statistics Act to add disabilities, both physical and mental, as factors that could be considered a basis for hate crimes. Although the Act originally mandated data collection for five years, the Church Arson Prevention Act of 1996 amended the collection duration "for each calendar year," making hate crime statistics a permanent addition to the UCR program. As with the other UCR data, law enforcement agencies contribute reports either directly or through their state reporting programs. Information contained in the data includes number of victims and offenders involved in each hate crime incident, type of victims, bias motivation, offense type, and location type.

Victims of gang-related homicides (total number of homicide victims; number of homicide victims - unknown gang-relation; number of homicide victims - known gang relation; number of gang-related homicide victims; percentage of gang-related homicide victims; rate (per 100,000 population) of gang-related homicide victims), Canada and regions, 1999 to 2024.

This dataset was compiled by the Illinois Criminal Justice Information Authority (ICJIA) at the request of the Governor’s Children’s Cabinet. This data contains the population of youth ages 13-26 in each county, the total population of each county, and the number and rate of index crimes reported, with domestic violence offenses and rates reported separately for every year between 2006 and 2015.

For the purpose of this analysis the crime data was gathered from the Illinois State Police Annual report Crime in Illinois. This publication is produced by the Illinois State Police every year using the UCR data that is submitted to them by individual jurisdictions throughout the state. The accuracy of this data presented is dependent on the local jurisdictions reporting their index crime and domestic violence offenses to ISP, so it can be included in the annual report.

Therefore, if there is large decrease in number of index crimes reported in the dataset it is likely that one or more jurisdictions did not report data for that year to ISP. If there is a large increase from year to year within a county it is likely that a jurisdiction within the county, who previously had not reported crime data, did report crime data for that year. If there is no reported crime in a certain year that means no jurisdictions, or a small jurisdiction with no crime from that county reported data to the Illinois State Police. The annual Crime in Illinois reports can be found on the ISP website www.isp.state.il.us.

A direct link to that annual reports is: http://www.isp.state.il.us/crime/ucrhome.cfm#anlrpts.

The Illinois Criminal Justice Information Authority did not record the data that is expressed in the dataset. ICJIA simply used the ISP reports to compile that yearly crime data into one chart that could be provided to the Illinois Governor’s Children’s Cabinet. This data set has be critically examined to be accurate according to the annual Crime in Illinois Reports. If there are issues with the data set provided please contact the Illinois State Police or the individual jurisdictions within a specific county.

**Index offenses do not include every crime event that occurs. Prior to 2014 there were 8 index crimes reported by the Illinois State Police in their annual reports, Criminal Homicide, Rape, Robbery, Aggravated Battery/Aggravated Assault, Burglary, Theft, Motor Vehicle Theft, and Arson. In 2014 there were two new offenses added to the list of index crimes these were Human Trafficking – Commercial Sex Acts and Human Trafficking – Involuntary Servitude. These are the index crimes that are recorded in the chart provided.

**“Domestic offenses are defined as offenses committed between family or household members. Family or household members include spouses; former spouses; parents; children; foster parents; foster children; legal guardians and their wards; stepchildren; other persons related by blood (aunt, uncle, cousin) or by present or previous marriage (in-laws); persons who share, or formerly shared, a common dwelling; persons who have, or allegedly have, a child in common; persons who share, or allegedly share, a blood relationship through a child; persons who have, or have had, a dating or engagement relationship; and persons with disabilities, their personal care assistants, or care givers outside the context of an employee of a public or private care facility. Every offense that occurs, when a domestic relationship exists between the victim and offender, must be reported (Illinois State Police).”

**“Offenses reported are not limited to domestic battery and violations of orders of protection; offenses most commonly associated with domestic violence (Illinois State Police).”

The crime rate was compiled using the total population, and the index crime. The Index crime whether all crime or Domestic Violence crime was divided by the total population then multiplied by 10,000, hence crime rate per 10,000.

The sources of data are the Illinois Uniform Crime Reporting Program and the U.S. Census Bureau.

The source of the description is the Illinois State Police and their Reporting guidelines and forms.

Measuring homicides across the world helps us understand violent crime and how people are affected by interpersonal violence.

But measuring homicides is challenging. Even homicide researchers do not always agree on whether the specific cause of death should be considered a homicide. Even when they agree on what counts as a homicide, it is difficult to count all of them.

In many countries, national civil registries do not certify most deaths or their cause. Besides lacking funds and personnel, a body has to be found to determine whether a death has happened. Authorities may also struggle to distinguish a homicide from a similar cause of death, such as an accident.

Law enforcement and criminal justice agencies collect more data on whether a death was unlawful — but their definition of unlawfulness may differ across countries and time.

Estimating homicides where neither of these sources is available or good enough is difficult. Estimates rely on inferences from similar countries and contextual factors that are based on strong assumptions. So how do researchers address these challenges and measure homicides?

In our work on homicides, we provide data from five main sources:

The WHO Mortality Database (WHO-MD)1 The Global Study on Homicide by the UN Office on Drugs and Crime (UNODC)2 The History of Homicide Database by Manuel Eisner (20033 and 20144) The Global Burden of Disease (GBD) study by the Institute for Health Metrics and Evaluation (IHME)5 The WHO Global Health Estimates (WHO-GHE)6 These sources all report homicides, cover many countries and years, and are frequently used by researchers and policymakers. They are not entirely separate, as they partially build upon each other.

The UNIFORM CRIME REPORTING PROGRAM DATA: OFFENSES KNOWN AND CLEARANCES BY ARREST, 2020 dataset is a compilation of offenses reported to law enforcement agencies in the United States. Due to the vast number of categories of crime committed in the United States, the FBI has limited the type of crimes included in this compilation to those crimes which people are most likely to report to police and those crimes which occur frequently enough to be analyzed across time. Crimes included are criminal homicide, forcible rape, robbery, aggravated assault, burglary, larceny-theft, and motor vehicle theft. Much information about these crimes is provided in this dataset. The number of times an offense has been reported, the number of reported offenses that have been cleared by arrests, and the number of cleared offenses which involved offenders under the age of 18 are the major items of information collected.

This data represents crime reported to the Seattle Police Department (SPD). Each row contains the record of a unique event where at least one criminal offense was reported by a member of the community or detected by an officer in the field. This data is the same data used in meetings such as SeaStat (https://www.seattle.gov/police/information-and-data/seastat) for strategic planning, accountability and performance management.

These data contain offenses and offense categorization coded to simulate the standard reported to the FBI under the National Incident Based Reporting System (NIBRS) and used to generate Uniform Crime Report (UCR) summary statistics. As these records evolve, daily and are continually refreshed, they will not match official UCR statistics. They represent a more accurate state of the record.

Previous versions of this data set have withheld approximately 40% of crimes. This updated process includes all records of crime reports logged in the Departments Records Management System (RMS) since 2008, which are tracked as part of the SeaStat process. In an effort to safeguard the privacy of our community, offense reports will only be located to the “beat” level. Location specific coordinates will no longer be provided.

Beats are the most granular unit of management used for patrol deployment. To learn more about patrol deployment, please visit: https://www.seattle.gov/police/about-us/about-policing/precinct-and-patrol-boundaries. In addition to the Departments patrol deployment areas, these data contain the “Neighborhood” where the crime occurred, if available. This coding is used to align crime data with the Micro Community Policing Plan (MCPP). For more information see: https://www.seattle.gov/police/community-policing/about-mcpp.

As with any data, certain condition and qualifications apply: 1) These data are refreshed, daily and represent the most accurate, evolved state of the record.

2) Due to quality control processes, these data will lag between 2 and 6 weeks. Most changes will occur within that record and reports logged in the last 2 weeks should be treated as volatile. Analysts may wish to remove these records from their analysis.

3) Not all offenses are reported here, only the primary offense as determined by the “Hierarchy Rule.” For more information on NIBRS and UCR, see the FBI (https://ucr.fbi.gov/nibrs-overview).

4) This dataset contains records of offenses that occurred prior to “go-live” of the existing RMS. Records are queried based on the full population of data and are not constrained by “Occurred Date.”

We invite you to engage these data, ask questions and explore.

This is a collection of Offenses Known and Clearances By Arrest data from 1960 to 2016. The monthly zip files contain one data file per year(57 total, 1960-2016) as well as a codebook for each year. These files have been read into R using the ASCII and setup files from ICPSR (or from the FBI for 2016 data) using the package asciiSetupReader. The end of the zip folder's name says what data type (R, SPSS, SAS, Microsoft Excel CSV, feather, Stata) the data is in. Due to file size limits on open ICPSR, not all file types were included for all the data.

The files are lightly cleaned. What this means specifically is that column names and value labels are standardized. In the original data column names were different between years (e.g. the December burglaries cleared column is "DEC_TOT_CLR_BRGLRY_TOT" in 1975 and "DEC_TOT_CLR_BURG_TOTAL" in 1977). The data here have standardized columns so you can compare between years and combine years together. The same thing is done for values inside of columns. For example, the state column gave state names in some years, abbreviations in others. For the code uses to clean and read the data, please see my GitHub file here. https://github.com/jacobkap/crime_data/blob/master/R_code/offenses_known.R

The zip files labeled "yearly" contain yearly data rather than monthly. These also contain far fewer descriptive columns about the agencies in an attempt to decrease file size. Each zip folder contains two files: a data file in whatever format you choose and a codebook. The data file is aggregated yearly and has already combined every year 1960-2016. For the code I used to do this, see here https://github.com/jacobkap/crime_data/blob/master/R_code/yearly_offenses_known.R.

If you find any mistakes in the data or have any suggestions, please email me at jkkaplan6@gmail.com

As a description of what UCR Offenses Known and Clearances By Arrest data contains, the following is copied from ICPSR's 2015 page for the data.

The Uniform Crime Reporting Program Data: Offenses Known and Clearances By Arrest dataset is a compilation of offenses reported to law enforcement agencies in the United States. Due to the vast number of categories of crime committed in the United States, the FBI has limited the type of crimes included in this compilation to those crimes which people are most likely to report to police and those crimes which occur frequently enough to be analyzed across time. Crimes included are criminal homicide, forcible rape, robbery, aggravated assault, burglary, larceny-theft, and motor vehicle theft. Much information about these crimes is provided in this dataset. The number of times an offense has been reported, the number of reported offenses that have been cleared by arrests, and the number of cleared offenses which involved offenders under the age of 18 are the major items of information collected.